Gram-Schmidt Q: Orthogonalizing v1 & v2 -Help Appreciated!

  • Thread starter astropi
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In summary, the conversation discusses the use of the Gram-Schmidt procedure to orthogonalize given vectors. It includes an attempt at a solution, which involves finding u2 and determining its dot product with u1. Ultimately, it is revealed that a mistake was made in the inner product calculation.
  • #1
astropi
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Homework Statement


First off, this isn't for a class, I'm just going over some material, however this does come from a textbook, so I figure this is a reasonable place to ask the question! Here's the question:

Use the Gram-Schmidt procedure to orthogonalize the following vectors:

v1=[(1+i),1,i]
v2=[i,3,1]
v3=[0,28,0]

Homework Equations


Let's not even worry about v3 right now. Let's just orthogonalize v1 and v2.

The Attempt at a Solution


First off, we let v1=u1 = [(1+i),1,i]

Now, we can find u2 by: [tex]u2 = v2 - \frac{<u1,v2>}{||u1||^2}u1[/tex]

The norm of u1 is 2, therefore squaring that we get 4.
When I took <u1,v2> I got 4. Therefore 4/4 = 1.
This leaves us with u2 = v2 - u1 = (-1,2,1-i)
HOWEVER, u2 dot u1 = 2
and of course if they were orthogonal they should equal 0.
Not sure where I made a mistake... so if anyone can help that would be appreciated!

cheers,

-astropi
 
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  • #2
(-1,2,1-i) dot ((1+i),1,i) is equal to zero. Not 2. I think you forgot a complex conjugate when you did the inner product.
 
  • #3
Dick said:
(-1,2,1-i) dot ((1+i),1,i) is equal to zero. Not 2. I think you forgot a complex conjugate when you did the inner product.

Yes, indeed...
my mind has been meandering around Hilbert space too long ;)
Thanks!
 
  • #4
astropi said:
Yes, indeed...
my mind has been meandering around Hilbert space too long ;)
Maybe you can be my tour guide :rofl:
 

1. What is the Gram-Schmidt process?

The Gram-Schmidt process is a mathematical method for orthogonalizing a set of vectors. It is named after mathematicians Jørgen Pedersen Gram and Erhard Schmidt.

2. How does the Gram-Schmidt process work?

The Gram-Schmidt process involves taking an initial set of linearly independent vectors and transforming them into a new set of orthogonal vectors. This is done by subtracting the components of each vector that are parallel to the previously orthogonalized vectors.

3. Why is orthogonalization important?

Orthogonalization is important because it allows us to work with simpler and more manageable sets of vectors. In many mathematical and scientific applications, orthogonal vectors are easier to analyze and manipulate than non-orthogonal vectors.

4. How is the Gram-Schmidt process used in practical applications?

The Gram-Schmidt process has numerous applications in mathematics and science, particularly in linear algebra and signal processing. It is often used to construct orthonormal bases for vector spaces, which are essential for solving many problems in these fields.

5. Are there any limitations or drawbacks to the Gram-Schmidt process?

One limitation of the Gram-Schmidt process is that it can be numerically unstable, meaning that small errors in the input vectors can lead to large errors in the output vectors. Additionally, the process can be computationally expensive for large sets of vectors.

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